1. Technical Field
The present invention relates to resizing an image into a high-resolution image, and more particularly to a forward and backward image resizing method for resizing an image twice and then reducing the resolution of the image to form a high-resolution image to be output.
2. Related Art
In many special environments, it is not easy to obtain a high-resolution image. For example, images obtained by low-resolution image capturing apparatus (resolution capability ranging from hundreds of thousands of pixels to millions of pixels), such as common web cameras, surveillance cameras, and the built-in cameras of low-end mobile phones, are often indistinct. In addition, network bandwidth limitations have the effect that when video information is played back as streaming media, the transmission of high-resolution image frames occupies the bandwidth of the internet transmission, resulting in low FPS (Frames Per Second); the video information is therefore generally transmitted in the format of low-resolution image frames. Moreover, as display sizes continue to increase, when the images are displayed on a large-sized display in a full-screen mode, the images become too blurred to view distinctly.
In view of these problems, many methods have been derived from image processing technologies, in which the low-resolution images are resized into a high-resolution image according to information contained therein, such that the resized images are not blurred.
In the prior art, common image resolution enhancement methods mainly include an interpolation method. In the interpolation method, the original image is resized into a high-resolution image by following steps. The first step is that known pixels are filled in values directly, and unknown pixels between the known pixels are reserved as blank pixels; next, the unknown pixels are predicted by combining the neighboring known pixels in different ways, and sequentially filled into the blank pixel blocks. The interpolation method has the advantage of high operational speed, but always causes over-smoothing, and produces a blurred visual appearance.
In addition to the interpolation method, the common image resolution enhancement methods also include an inverse image modeling method and a training method.
In the inverse image modeling method, it is assumed that the generation of a low-resolution image is a series of image processing procedures. In other words, it is assumed that a known low-resolution image is generated from a high-resolution image by blurring, down-sampling, noise reduction, and other processes, so that a generation model for the low-resolution image can be established to convert the high-resolution image into the known low-resolution image. In the inverse image modeling method, an inverse generation model for resizing the low-resolution image into the high-resolution image is further established inversely according to the generation model for the low-resolution image, so as to estimate an unknown fuzzy filter to restore the original high-resolution image. The inverse image modeling method is characterized by a sharp presentation at the image edges; however, since the inverse generation model aims to obtain an optimal solution and requires multiple iterations, the operation time is long and required computational effort is high.
The training method is to collect a large number of corresponding low-resolution and high-resolution training images, memorize and train a corresponding relation between each low-resolution local texture and each high-resolution local texture, and use the relation to construct a database.
When a low-resolution non-training image is processed by the training method, in the first step the image is divided into many blocks, and the database is searched for low-resolution training blocks having the most similar texture features to each block; and then corresponding high-resolution blocks are found, so as to construct a high-resolution image. In the training method the prediction is based on real images, so the visual perception of the output result is natural; however, in different situations the presentation is largely dependent on the database, and the database training and searching processes require a long time.
Moreover, the image resolution enhancement methods in the prior art are applied to undistorted images, and are scarcely applied to distorted images. As the size of the display continuously increases, when images in the conventional streaming media format are displayed in a full-screen mode, blurring occurs. The most serious problem encountered when common algorithms are applied to distorted images is that noise and defects are also enlarged. However, if noise and defect filtering is performed prior to the resolution enhancement process, details in the low-resolution image will be lost, and an over-smoothing effect is observed after enlargement, thereby reducing the effect of resolution enhancement.